Archive for the ‘data visualization’ Category

Blogging about a cutting-edge subject is so much fun, especially with web 2.0 tools that can potentially tell you interesting stuff about whats going on in the works. I started this blog since moving to Denmark in Sep ’09 , and I just had a look today at the Cluster Map app installed on my blog. Here is the verdict – visitors come from everywhere! I would love to see some big red dots on North and central Africa, Russia and Central America as well, though.

Incidentally, I just ran into a map of members on the Interaction Design Association (IXDA.org) website. Its here below; see any patterns?

Working with Intel design researcher Jay Mellican (of Intel’s Digital Home group) and CIID faculty Vinay Venkatraman, we explored the role of the social collective in achieving sustainable behaviors towards the effective management of energy. Here I will discuss the context of the project, and go into our process and solutions in future posts.

The focus for Intel was the emerging “smart grid” – the efforts promoted by many governments and utilities to modernize, from the bottom up, the integrated system by which energy is collected and distributed. In bringing the energy grids of yesteryear into the digital age, many of the technologies and standards that will make up the “smart grid” are yet to be defined, and the implications they will have on our patterns of daily living, as well as their likely success, will depend heavily on how they are defined.

The idea of the “smart grid” is a really interesting one. Here is a really nice video that covers the main aspects of the concept well:

Its hard to discuss the “smart grid” for long today without running into GE’s efforts in that space. As part of GE’s ecomagination campaign, the company has created this engaging augmented reality web object, as shown in the video below:

Given all that, in trying to envision scenarios and solutions for behavioral change for this emerging and very complex space, my team (classmate Mimi and I) quickly realized that the problem of visibility of use was a crucial one. According to the Environment Change Institute, for instance, “most householders have only a vague idea of how much energy they are using for different purposes, hence the importance of making energy flows more visible and controllable. There is a lot of interest in the potential for better feedback using improved (‘smart’) metering, more informative billing and direct display panels.”

As my team’s interest was in understanding the behaviors and influence of the “social collective” – networks of people connected by social technologies – in the context of smart energy use, we decided to explore the space beyond the use of individual control devices such as energy meters, and look at “visibility of use” aspects for groups of people. More on our contextual research and enquiry in posts to come.

Project Context
The brief for this class project (with Shawn Allen of Stamen Design, SF) was to develop interactive data visualizations based on UN Data as available on http://data.un.org/. On this website, the UN provides downloadable data in various ready-to-use formats on a wide variety of issues and themes – demographics, global indicators and statistics on commodities and trade, energy, population and gender, industries, children, health, tourism etc.

Choosing a data setWe were interested in several data sets to begin with, but common underlying themes seemed to dominate across our choices. In order to be able to think freely about the kinds of data that we would like to work with, we also explored data sets external to those provided by the UN. These included experimenting with data from Last.fm (a streaming internet radio service) and with live feed of statistics describing developments on Second Life (a globally popular virtual world).

Finally, we decided to go with a data set that provided ‘Tourist Arrivals by Region of Origin’ for the years 2001-2005 from the UN database.

It is usually easy to find information about the most popular global tourist destinations. What is less understood is ‘who travels where’, or to put it in broader terms, which travelers place the world’s top tourist destinations at the top of the charts. Since we were focused from the first on exploring the ‘supply side’ of the business, we decided to include only the world’s top twenty five tourist destinations (by revenue from tourism).

The Visualization

Global Tourists - Map View

Global Tourists - Stack View

We chose to use the metaphor of a world map for this visualization to be able to simultaneously represent both a region of tourist origin as well those tourism hotspots most frequented by its travelers. The region of origin chosen is depicted by a color, while circles of the corresponding color represent the places visited by its native tourist population. The size of the circle indicates volume of tourist traffic.
Viewers can choose to see the visualization in either a ‘map’ (Image 01) or a ‘stack’ (Image 02) view. Stacks can further be organized by ‘Destination’ or ‘Number of Visitors’; switching between these two modes reveals interesting trends across the years.

Key Learnings from VisualizationThe key learning that is evident from browsing the visualization is that global tourism is still largely regional. Tourists from the Americas travel largely within the Americas, with some concentrated bursts of travelers to some parts of Europe. Europeans travel mostly within Europe, as do Asians and Africans within their own regions. If there are far-off exceptions to this overall trend, they are limited in number and usually to a very specific set of destinations depending on the region of origin.
Since data was only available for a six year period, we (predictably) saw no huge variations from year to year, especially in the map view. In the stack view however, one notices that competition for tourist revenue is fierce amongst the top twenty five destinations, indicated by the quite frequent shifts in place among the contenders under the fifth spot.

This mode is the opposite approach to an interactive timeline/map approach, where all of the information would be available upfront, and the user learns primarily by exploring the interface. In which case, the customization made available to users is gained by trading off the goal to communicate something very specific as is achieved by this visualization.

Come to think of it, it is surprisingly difficult to go beyond the handful of infoviz presentation formats in currency today. Maps, charts, graphs, clouds, trees and network diagrams seem to dominate in different forms and variations. And this is true for two reasons – its incredibly hard to find new metaphors that do a great job of representing qualitative information, and secondly, I suspect it has a lot to do with our own preferred ways of ‘seeing meaning’ – the information scanning, browsing and seeking behaviors we are most attuned to.

For instance, this is a list of Visualization Types provided by IBM’s ‘Many Eyes’. While the formats are decidely limited, the possibilities of exploiting these formats to present various types and degrees of qualitative information (as suggested by the titles they are grouped under) catch my eye.